A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems

• Published in: Geomatics, natural hazards and risk Vol. 14; no. 1
• Main Authors: Hanadé Houmma, Ismaguil, Gadal, Sébastien, El Mansouri, Loubna, Garba, Maman, Gbetkom, Paul Gérard, Mamane Barkawi, Mansour Badamassi, Hadria, Rachid
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Agricultural drought; Agroclimatic conditions; Algorithms; Artificial Intelligence; Climatic indexes; Computer Science; Correlation; Crop yield; Drought; Drought index; Drought monitoring; Engineering Sciences; Environmental conditions; Environmental Engineering; Environmental Sciences; Geography; Global Changes; Heat stress; Heat tolerance; Humanities and Social Sciences; Modelling; Multivariate analysis; multivariate index; Precipitation; Rain; Rainfall; random forest; Remote sensing; Sahelian; Signal and Image processing; Statistical analysis; Water deficit; Water stress; NOAA AVHRR; Multivariate index; Remote Sensing; VIIRS; Sahelian Agro-Systems; Random forest; Land surface temperature; Drought; NDVI; MODIS
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2023.2223384

This manuscript aims to develop a new multivariate composite index for monitoring agricultural drought. To achieve this, the AVHRR, VIIRS, CHIRPS data series over a period of 40 years, rainfall and crop yield data as references were used. Variables include parameters for vegetative stress (SVCI, PV, SMN), water stress (PCI, RDI, NRDI), and heat stress (SMT, TCI, STCI), and a new variable related to environmental conditions was calculated through a normalized rainfall efficiency index. Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. The multivariate composite model was compared to the VHI, NVSWI and SPI-12 indices for validation. The results show a large spatiotemporal concordance between the MDCI and the validation indices with a maximum correlation of 0.95 with the VHI and a highly significant p value (< 2.2e-16). Validation of the MDCI model by SPI-12 shows a significantly higher statistically significant relationship than that observed between SPI and VHI and NVSWI. P value range from 3.531e-05 to 6.137e-06 with correlations that vary between 0.6 and 0.64 depending on the station. It is also highly correlated with the Palmer drought severity index (PDSI) and climatic water deficit index (CWDI), with R = 0.85 and p value < 5.8e-10 and R = 0.72 and p value < 1.9e-6, respectively. Finally, the study provides a new direction for multivariate modeling of agricultural drought that should be further explored under various agroclimatic conditions.